Overview

Dataset statistics

Number of variables17
Number of observations1003
Missing cells51
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory133.3 KiB
Average record size in memory136.1 B

Variable types

Categorical5
Numeric12

Alerts

title has a high cardinality: 945 distinct values High cardinality
artist has a high cardinality: 444 distinct values High cardinality
top genre has a high cardinality: 132 distinct values High cardinality
year released is highly correlated with top yearHigh correlation
nrgy is highly correlated with dBHigh correlation
dB is highly correlated with nrgyHigh correlation
top year is highly correlated with year releasedHigh correlation
year released is highly correlated with top yearHigh correlation
nrgy is highly correlated with dB and 1 other fieldsHigh correlation
dB is highly correlated with nrgyHigh correlation
acous is highly correlated with nrgyHigh correlation
top year is highly correlated with year releasedHigh correlation
year released is highly correlated with top yearHigh correlation
nrgy is highly correlated with dBHigh correlation
dB is highly correlated with nrgyHigh correlation
top year is highly correlated with year releasedHigh correlation
year released is highly correlated with added and 1 other fieldsHigh correlation
added is highly correlated with year released and 2 other fieldsHigh correlation
bpm is highly correlated with dnceHigh correlation
nrgy is highly correlated with dB and 1 other fieldsHigh correlation
dnce is highly correlated with bpmHigh correlation
dB is highly correlated with added and 2 other fieldsHigh correlation
acous is highly correlated with nrgy and 1 other fieldsHigh correlation
top year is highly correlated with year released and 1 other fieldsHigh correlation
title is uniformly distributed Uniform
acous has 134 (13.4%) zeros Zeros

Reproduction

Analysis started2022-04-14 15:28:25.714465
Analysis finished2022-04-14 15:29:21.487949
Duration55.77 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

title
Categorical

HIGH CARDINALITY
UNIFORM

Distinct945
Distinct (%)94.5%
Missing3
Missing (%)0.3%
Memory size8.0 KiB
Don't
 
3
Sorry
 
3
Paradise
 
3
Team
 
2
No Hands (feat. Roscoe Dash & Wale)
 
2
Other values (940)
987 

Length

Max length94
Median length13
Mean length17.249
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique893 ?
Unique (%)89.3%

Sample

1st rowSTARSTRUKK (feat. Katy Perry)
2nd rowMy First Kiss (feat. Ke$ha)
3rd rowI Need A Dollar
4th rowAirplanes (feat. Hayley Williams of Paramore)
5th rowNothin' on You (feat. Bruno Mars)

Common Values

ValueCountFrequency (%)
Don't3
 
0.3%
Sorry3
 
0.3%
Paradise3
 
0.3%
Team2
 
0.2%
No Hands (feat. Roscoe Dash & Wale)2
 
0.2%
Better Now2
 
0.2%
lovely (with Khalid)2
 
0.2%
All My Friends (feat. Tinashe & Chance the Rapper)2
 
0.2%
Stole the Show2
 
0.2%
I Knew You Were Trouble.2
 
0.2%
Other values (935)977
97.4%
(Missing)3
 
0.3%

Length

2022-04-14T10:29:21.796087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
feat172
 
5.2%
111
 
3.3%
the92
 
2.8%
you60
 
1.8%
love55
 
1.7%
me51
 
1.5%
i48
 
1.4%
it38
 
1.1%
edit34
 
1.0%
my33
 
1.0%
Other values (1290)2629
79.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

artist
Categorical

HIGH CARDINALITY

Distinct444
Distinct (%)44.4%
Missing3
Missing (%)0.3%
Memory size8.0 KiB
Taylor Swift
 
21
Drake
 
18
Calvin Harris
 
18
Rihanna
 
14
Ariana Grande
 
14
Other values (439)
915 

Length

Max length24
Median length10
Mean length10.03
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique250 ?
Unique (%)25.0%

Sample

1st row3OH!3
2nd row3OH!3
3rd rowAloe Blacc
4th rowB.o.B
5th rowB.o.B

Common Values

ValueCountFrequency (%)
Taylor Swift21
 
2.1%
Drake18
 
1.8%
Calvin Harris18
 
1.8%
Rihanna14
 
1.4%
Ariana Grande14
 
1.4%
Bruno Mars13
 
1.3%
Maroon 511
 
1.1%
Post Malone10
 
1.0%
Chris Brown10
 
1.0%
Jason Derulo10
 
1.0%
Other values (434)861
85.8%

Length

2022-04-14T10:29:22.115888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the50
 
2.8%
taylor21
 
1.2%
swift21
 
1.2%
20
 
1.1%
harris18
 
1.0%
drake18
 
1.0%
calvin18
 
1.0%
515
 
0.8%
rihanna14
 
0.8%
ariana14
 
0.8%
Other values (686)1587
88.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

top genre
Categorical

HIGH CARDINALITY

Distinct132
Distinct (%)13.2%
Missing3
Missing (%)0.3%
Memory size8.0 KiB
dance pop
361 
pop
57 
atl hip hop
 
39
art pop
 
37
boy band
 
21
Other values (127)
485 

Length

Max length25
Median length9
Mean length9.983
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)5.2%

Sample

1st rowdance pop
2nd rowdance pop
3rd rowpop soul
4th rowatl hip hop
5th rowatl hip hop

Common Values

ValueCountFrequency (%)
dance pop361
36.0%
pop57
 
5.7%
atl hip hop39
 
3.9%
art pop37
 
3.7%
boy band21
 
2.1%
hip hop21
 
2.1%
canadian hip hop18
 
1.8%
edm17
 
1.7%
folk-pop15
 
1.5%
contemporary country14
 
1.4%
Other values (122)400
39.9%

Length

2022-04-14T10:29:22.830987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pop527
26.3%
dance372
18.6%
hip127
 
6.3%
hop126
 
6.3%
rap73
 
3.6%
canadian42
 
2.1%
rock40
 
2.0%
atl39
 
1.9%
art37
 
1.8%
modern27
 
1.3%
Other values (129)592
29.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

year released
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)1.4%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean2014.39
Minimum1975
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2022-04-14T10:29:23.154762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1975
5-th percentile2010
Q12012
median2014
Q32017
95-th percentile2019
Maximum2021
Range46
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.241358728
Coefficient of variation (CV)0.001609101876
Kurtosis20.2898332
Mean2014.39
Median Absolute Deviation (MAD)3
Skewness-1.776445005
Sum2014390
Variance10.50640641
MonotonicityNot monotonic
2022-04-14T10:29:23.453026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2018112
11.2%
2012109
10.9%
2014101
10.1%
201799
9.9%
201599
9.9%
201094
9.4%
201193
9.3%
201987
8.7%
201386
8.6%
201686
8.6%
Other values (4)34
 
3.4%
ValueCountFrequency (%)
19751
 
0.1%
200924
 
2.4%
201094
9.4%
201193
9.3%
2012109
10.9%
201386
8.6%
2014101
10.1%
201599
9.9%
201686
8.6%
201799
9.9%
ValueCountFrequency (%)
20213
 
0.3%
20206
 
0.6%
201987
8.7%
2018112
11.2%
201799
9.9%
201686
8.6%
201599
9.9%
2014101
10.1%
201386
8.6%
2012109
10.9%

added
Categorical

HIGH CORRELATION

Distinct26
Distinct (%)2.6%
Missing3
Missing (%)0.3%
Memory size8.0 KiB
2020‑06‑22
276 
2022‑02‑17
100 
2020‑06‑11
100 
2020‑06‑16
99 
2020‑06‑10
94 
Other values (21)
331 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.8%

Sample

1st row2022‑02‑17
2nd row2022‑02‑17
3rd row2022‑02‑17
4th row2022‑02‑17
5th row2022‑02‑17

Common Values

ValueCountFrequency (%)
2020‑06‑22276
27.5%
2022‑02‑17100
 
10.0%
2020‑06‑11100
 
10.0%
2020‑06‑1699
 
9.9%
2020‑06‑1094
 
9.4%
2021‑01‑2891
 
9.1%
2020‑06‑0887
 
8.7%
2020‑06‑1985
 
8.5%
2021‑08‑0914
 
1.4%
2021‑08‑1710
 
1.0%
Other values (16)44
 
4.4%

Length

2022-04-14T10:29:23.785247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020‑06‑22276
27.6%
2020‑06‑11100
 
10.0%
2022‑02‑17100
 
10.0%
2020‑06‑1699
 
9.9%
2020‑06‑1094
 
9.4%
2021‑01‑2891
 
9.1%
2020‑06‑0887
 
8.7%
2020‑06‑1985
 
8.5%
2021‑08‑0914
 
1.4%
2021‑08‑1710
 
1.0%
Other values (16)44
 
4.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

bpm
Real number (ℝ≥0)

HIGH CORRELATION

Distinct122
Distinct (%)12.2%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean121.262
Minimum65
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2022-04-14T10:29:24.186339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile82
Q1100
median122
Q3134
95-th percentile172
Maximum206
Range141
Interquartile range (IQR)34

Descriptive statistics

Standard deviation26.23802183
Coefficient of variation (CV)0.216374642
Kurtosis0.2452450595
Mean121.262
Median Absolute Deviation (MAD)18
Skewness0.5494200137
Sum121262
Variance688.4337898
MonotonicityNot monotonic
2022-04-14T10:29:24.661613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12857
 
5.7%
12055
 
5.5%
10040
 
4.0%
12535
 
3.5%
12633
 
3.3%
13033
 
3.3%
14023
 
2.3%
9522
 
2.2%
12220
 
2.0%
9817
 
1.7%
Other values (112)665
66.3%
ValueCountFrequency (%)
652
 
0.2%
691
 
0.1%
722
 
0.2%
732
 
0.2%
743
0.3%
756
0.6%
765
0.5%
776
0.6%
782
 
0.2%
795
0.5%
ValueCountFrequency (%)
2062
 
0.2%
2041
 
0.1%
2022
 
0.2%
2001
 
0.1%
1981
 
0.1%
1962
 
0.2%
1922
 
0.2%
1906
0.6%
1882
 
0.2%
1871
 
0.1%

nrgy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct80
Distinct (%)8.0%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean69.502
Minimum6
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2022-04-14T10:29:25.102571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile41
Q159
median71
Q381.25
95-th percentile92
Maximum98
Range92
Interquartile range (IQR)22.25

Descriptive statistics

Standard deviation15.96141481
Coefficient of variation (CV)0.229654036
Kurtosis0.1802383136
Mean69.502
Median Absolute Deviation (MAD)11
Skewness-0.65086111
Sum69502
Variance254.7667628
MonotonicityNot monotonic
2022-04-14T10:29:25.736302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7935
 
3.5%
7330
 
3.0%
7130
 
3.0%
8129
 
2.9%
7827
 
2.7%
8026
 
2.6%
8226
 
2.6%
8425
 
2.5%
5924
 
2.4%
7624
 
2.4%
Other values (70)724
72.2%
ValueCountFrequency (%)
61
0.1%
111
0.1%
151
0.1%
161
0.1%
171
0.1%
231
0.1%
252
0.2%
261
0.1%
271
0.1%
282
0.2%
ValueCountFrequency (%)
982
 
0.2%
974
 
0.4%
964
 
0.4%
957
 
0.7%
9410
1.0%
9317
1.7%
9216
1.6%
9113
1.3%
907
 
0.7%
8923
2.3%

dnce
Real number (ℝ≥0)

HIGH CORRELATION

Distinct68
Distinct (%)6.8%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean66.876
Minimum19
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2022-04-14T10:29:26.206345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile42
Q159
median68
Q375
95-th percentile88
Maximum96
Range77
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.12192093
Coefficient of variation (CV)0.196212706
Kurtosis0.0936383514
Mean66.876
Median Absolute Deviation (MAD)8
Skewness-0.411284933
Sum66876
Variance172.1848088
MonotonicityNot monotonic
2022-04-14T10:29:26.549213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6742
 
4.2%
6638
 
3.8%
6837
 
3.7%
6937
 
3.7%
7537
 
3.7%
7336
 
3.6%
6535
 
3.5%
7234
 
3.4%
6131
 
3.1%
7030
 
3.0%
Other values (58)643
64.1%
ValueCountFrequency (%)
191
 
0.1%
261
 
0.1%
281
 
0.1%
314
0.4%
331
 
0.1%
341
 
0.1%
355
0.5%
364
0.4%
376
0.6%
387
0.7%
ValueCountFrequency (%)
962
 
0.2%
951
 
0.1%
942
 
0.2%
937
0.7%
925
 
0.5%
9110
1.0%
907
0.7%
8913
1.3%
888
0.8%
877
0.7%

dB
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)1.6%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean-5.663
Minimum-18
Maximum-1
Zeros0
Zeros (%)0.0%
Negative1000
Negative (%)99.7%
Memory size8.0 KiB
2022-04-14T10:29:26.856228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-18
5-th percentile-9
Q1-7
median-5
Q3-4
95-th percentile-3
Maximum-1
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.02522407
Coefficient of variation (CV)-0.3576238866
Kurtosis2.9972501
Mean-5.663
Median Absolute Deviation (MAD)1
Skewness-1.156751472
Sum-5663
Variance4.101532533
MonotonicityNot monotonic
2022-04-14T10:29:27.072120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
-6212
21.1%
-5203
20.2%
-4201
20.0%
-7128
12.8%
-390
9.0%
-867
 
6.7%
-939
 
3.9%
-1018
 
1.8%
-215
 
1.5%
-1114
 
1.4%
Other values (6)13
 
1.3%
ValueCountFrequency (%)
-181
 
0.1%
-153
 
0.3%
-141
 
0.1%
-134
 
0.4%
-122
 
0.2%
-1114
 
1.4%
-1018
 
1.8%
-939
 
3.9%
-867
6.7%
-7128
12.8%
ValueCountFrequency (%)
-12
 
0.2%
-215
 
1.5%
-390
9.0%
-4201
20.0%
-5203
20.2%
-6212
21.1%
-7128
12.8%
-867
 
6.7%
-939
 
3.9%
-1018
 
1.8%

live
Real number (ℝ≥0)

Distinct67
Distinct (%)6.7%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean17.911
Minimum2
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2022-04-14T10:29:27.351104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q19
median12
Q323
95-th percentile44
Maximum83
Range81
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.43151088
Coefficient of variation (CV)0.7499029021
Kurtosis4.072649065
Mean17.911
Median Absolute Deviation (MAD)4
Skewness1.916292267
Sum17911
Variance180.4054845
MonotonicityNot monotonic
2022-04-14T10:29:27.821857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1193
 
9.3%
980
 
8.0%
872
 
7.2%
1071
 
7.1%
1269
 
6.9%
1344
 
4.4%
743
 
4.3%
638
 
3.8%
1436
 
3.6%
1632
 
3.2%
Other values (57)422
42.1%
ValueCountFrequency (%)
24
 
0.4%
36
 
0.6%
47
 
0.7%
521
 
2.1%
638
3.8%
743
4.3%
872
7.2%
980
8.0%
1071
7.1%
1193
9.3%
ValueCountFrequency (%)
831
 
0.1%
821
 
0.1%
801
 
0.1%
742
0.2%
701
 
0.1%
694
0.4%
672
0.2%
661
 
0.1%
654
0.4%
641
 
0.1%

val
Real number (ℝ≥0)

Distinct93
Distinct (%)9.3%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean50.901
Minimum4
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2022-04-14T10:29:28.118052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile17
Q135
median50.5
Q368
95-th percentile87
Maximum97
Range93
Interquartile range (IQR)33

Descriptive statistics

Standard deviation21.56339906
Coefficient of variation (CV)0.4236340949
Kurtosis-0.8563255672
Mean50.901
Median Absolute Deviation (MAD)16.5
Skewness0.0477864545
Sum50901
Variance464.9801792
MonotonicityNot monotonic
2022-04-14T10:29:28.591910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4526
 
2.6%
7422
 
2.2%
3521
 
2.1%
5421
 
2.1%
4220
 
2.0%
3419
 
1.9%
5919
 
1.9%
4019
 
1.9%
3619
 
1.9%
4618
 
1.8%
Other values (83)796
79.4%
ValueCountFrequency (%)
41
 
0.1%
51
 
0.1%
71
 
0.1%
84
0.4%
91
 
0.1%
106
0.6%
118
0.8%
124
0.4%
135
0.5%
145
0.5%
ValueCountFrequency (%)
972
 
0.2%
964
 
0.4%
953
 
0.3%
944
 
0.4%
938
0.8%
923
 
0.3%
912
 
0.2%
902
 
0.2%
894
 
0.4%
8813
1.3%

dur
Real number (ℝ≥0)

Distinct176
Distinct (%)17.6%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean220.406
Minimum113
Maximum688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2022-04-14T10:29:29.326960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum113
5-th percentile169.95
Q1197
median216
Q3237
95-th percentile288
Maximum688
Range575
Interquartile range (IQR)40

Descriptive statistics

Standard deviation39.92767691
Coefficient of variation (CV)0.1811551269
Kurtosis21.12107027
Mean220.406
Median Absolute Deviation (MAD)20
Skewness2.406328882
Sum220406
Variance1594.219383
MonotonicityNot monotonic
2022-04-14T10:29:30.369357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20121
 
2.1%
21421
 
2.1%
22818
 
1.8%
19917
 
1.7%
20617
 
1.7%
20017
 
1.7%
22117
 
1.7%
21517
 
1.7%
23416
 
1.6%
20216
 
1.6%
Other values (166)823
82.1%
ValueCountFrequency (%)
1131
 
0.1%
1151
 
0.1%
1191
 
0.1%
1221
 
0.1%
1241
 
0.1%
1261
 
0.1%
1291
 
0.1%
1313
0.3%
1351
 
0.1%
1371
 
0.1%
ValueCountFrequency (%)
6881
0.1%
4841
0.1%
4181
0.1%
3542
0.2%
3501
0.1%
3481
0.1%
3431
0.1%
3381
0.1%
3371
0.1%
3321
0.1%

acous
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct89
Distinct (%)8.9%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean14.369
Minimum0
Maximum98
Zeros134
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2022-04-14T10:29:32.084058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q319
95-th percentile59
Maximum98
Range98
Interquartile range (IQR)17

Descriptive statistics

Standard deviation19.45403039
Coefficient of variation (CV)1.353888954
Kurtosis4.312550104
Mean14.369
Median Absolute Deviation (MAD)5
Skewness2.078699941
Sum14369
Variance378.4592983
MonotonicityNot monotonic
2022-04-14T10:29:33.104769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0134
 
13.4%
1108
 
10.8%
275
 
7.5%
368
 
6.8%
452
 
5.2%
543
 
4.3%
634
 
3.4%
1130
 
3.0%
928
 
2.8%
828
 
2.8%
Other values (79)400
39.9%
ValueCountFrequency (%)
0134
13.4%
1108
10.8%
275
7.5%
368
6.8%
452
 
5.2%
543
 
4.3%
634
 
3.4%
721
 
2.1%
828
 
2.8%
928
 
2.8%
ValueCountFrequency (%)
981
 
0.1%
971
 
0.1%
952
0.2%
941
 
0.1%
934
0.4%
922
0.2%
901
 
0.1%
882
0.2%
871
 
0.1%
861
 
0.1%

spch
Real number (ℝ≥0)

Distinct50
Distinct (%)5.0%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean10.064
Minimum2
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2022-04-14T10:29:33.450304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median6
Q312
95-th percentile31.05
Maximum53
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.276743069
Coefficient of variation (CV)0.9217749472
Kurtosis3.780643668
Mean10.064
Median Absolute Deviation (MAD)3
Skewness1.9870704
Sum10064
Variance86.05796196
MonotonicityNot monotonic
2022-04-14T10:29:33.717999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4175
17.4%
3138
13.8%
5138
13.8%
669
 
6.9%
756
 
5.6%
951
 
5.1%
847
 
4.7%
1038
 
3.8%
1425
 
2.5%
1124
 
2.4%
Other values (40)239
23.8%
ValueCountFrequency (%)
23
 
0.3%
3138
13.8%
4175
17.4%
5138
13.8%
669
 
6.9%
756
 
5.6%
847
 
4.7%
951
 
5.1%
1038
 
3.8%
1124
 
2.4%
ValueCountFrequency (%)
531
 
0.1%
521
 
0.1%
511
 
0.1%
491
 
0.1%
481
 
0.1%
471
 
0.1%
462
0.2%
452
0.2%
441
 
0.1%
433
0.3%

pop
Real number (ℝ≥0)

Distinct55
Distinct (%)5.5%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean74.84
Minimum35
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2022-04-14T10:29:34.049532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile59
Q170
median76
Q381
95-th percentile87
Maximum95
Range60
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.807836169
Coefficient of variation (CV)0.1176888852
Kurtosis1.504825813
Mean74.84
Median Absolute Deviation (MAD)5
Skewness-0.9334162419
Sum74840
Variance77.57797798
MonotonicityNot monotonic
2022-04-14T10:29:34.403450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7764
 
6.4%
7854
 
5.4%
8151
 
5.1%
7448
 
4.8%
8046
 
4.6%
7646
 
4.6%
7145
 
4.5%
7543
 
4.3%
8341
 
4.1%
8239
 
3.9%
Other values (45)523
52.1%
ValueCountFrequency (%)
351
0.1%
392
0.2%
401
0.1%
411
0.1%
421
0.1%
431
0.1%
441
0.1%
451
0.1%
461
0.1%
481
0.1%
ValueCountFrequency (%)
951
 
0.1%
941
 
0.1%
916
 
0.6%
902
 
0.2%
8912
 
1.2%
8818
1.8%
8716
1.6%
8629
2.9%
8532
3.2%
8434
3.4%

top year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)1.0%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean2014.5
Minimum2010
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2022-04-14T10:29:34.791852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12012
median2014.5
Q32017
95-th percentile2019
Maximum2019
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.873718542
Coefficient of variation (CV)0.001426517023
Kurtosis-1.224361662
Mean2014.5
Median Absolute Deviation (MAD)2.5
Skewness0
Sum2014500
Variance8.258258258
MonotonicityIncreasing
2022-04-14T10:29:35.081081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2010100
10.0%
2011100
10.0%
2012100
10.0%
2013100
10.0%
2014100
10.0%
2015100
10.0%
2016100
10.0%
2017100
10.0%
2018100
10.0%
2019100
10.0%
(Missing)3
 
0.3%
ValueCountFrequency (%)
2010100
10.0%
2011100
10.0%
2012100
10.0%
2013100
10.0%
2014100
10.0%
2015100
10.0%
2016100
10.0%
2017100
10.0%
2018100
10.0%
2019100
10.0%
ValueCountFrequency (%)
2019100
10.0%
2018100
10.0%
2017100
10.0%
2016100
10.0%
2015100
10.0%
2014100
10.0%
2013100
10.0%
2012100
10.0%
2011100
10.0%
2010100
10.0%

artist type
Categorical

Distinct4
Distinct (%)0.4%
Missing3
Missing (%)0.3%
Memory size8.0 KiB
Solo
748 
Band/Group
169 
Duo
 
70
Trio
 
13

Length

Max length10
Median length4
Mean length4.944
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDuo
2nd rowDuo
3rd rowSolo
4th rowSolo
5th rowSolo

Common Values

ValueCountFrequency (%)
Solo748
74.6%
Band/Group169
 
16.8%
Duo70
 
7.0%
Trio13
 
1.3%
(Missing)3
 
0.3%

Length

2022-04-14T10:29:35.266069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T10:29:35.445604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
solo748
74.8%
band/group169
 
16.9%
duo70
 
7.0%
trio13
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-14T10:29:15.099502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:28.030265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:33.049160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:36.961753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:40.680449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:44.177364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:47.490395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:50.865147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:54.340916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:57.674728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:01.943792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:06.849437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:15.400000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:28.688165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:33.389884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:37.272587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:41.063362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:44.413507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:47.785023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:51.146195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:54.632062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:57.886257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:02.190867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:07.731698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:15.664475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:29.413574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:33.693475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:37.538594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:41.379143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:44.938803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:48.044413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:51.463115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:54.927490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:58.106028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:02.426884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:08.478931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:15.948085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:30.183710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:33.968950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:37.766021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:41.600192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:45.131424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:48.437999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:51.715360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:55.265051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:58.350931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:02.730100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:08.949221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:16.203700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:30.639586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:34.309819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:38.059895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:41.908410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:45.399951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:48.865263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:52.275904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:55.608275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:58.815971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:03.044096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:09.873669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:16.513353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:30.999397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:34.586124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:38.451763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:42.192894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:45.706544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:49.094908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:52.546391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:55.873054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:59.686454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:03.329361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:10.828841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:16.879157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:31.326302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:34.863435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:38.813946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:42.521401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:45.973329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:49.427214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:52.842039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:56.171033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:59.983137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:03.594954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:11.743412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:17.170256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:31.575666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:35.189547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:39.294671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:42.801955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:46.266185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:49.637542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:53.043873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:56.447684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:00.544602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:03.933159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:12.470153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:17.410126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:31.907934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:35.433341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:39.644280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:43.090388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:46.556392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:49.924189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:53.284882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:56.689839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:00.851791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:04.464432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:13.388297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:17.634189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:32.228479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:35.711121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:39.895104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:43.376815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:46.798535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:50.192940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:53.600304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:56.930200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:01.082629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:04.748428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:13.966692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:17.832364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:32.484671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:36.027100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:40.167391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:43.612528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:47.048518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:50.423577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:53.845912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:57.194781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:01.405248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:05.320811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:14.594473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:18.111878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:32.750864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:36.727617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:40.430078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:43.919227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:47.259038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:50.664876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:54.096626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:28:57.405328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:01.711892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:06.127731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T10:29:14.829725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-14T10:29:35.577917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-14T10:29:35.975367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-14T10:29:36.413392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-14T10:29:37.188174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-14T10:29:37.443567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-14T10:29:18.576200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-14T10:29:19.166709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-14T10:29:19.936459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-14T10:29:21.100289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

titleartisttop genreyear releasedaddedbpmnrgydncedBlivevalduracousspchpoptop yearartist type
0STARSTRUKK (feat. Katy Perry)3OH!3dance pop2009.02022‑02‑17140.081.061.0-6.023.023.0203.00.06.070.02010.0Duo
1My First Kiss (feat. Ke$ha)3OH!3dance pop2010.02022‑02‑17138.089.068.0-4.036.083.0192.01.08.068.02010.0Duo
2I Need A DollarAloe Blaccpop soul2010.02022‑02‑1795.048.084.0-7.09.096.0243.020.03.072.02010.0Solo
3Airplanes (feat. Hayley Williams of Paramore)B.o.Batl hip hop2010.02022‑02‑1793.087.066.0-4.04.038.0180.011.012.080.02010.0Solo
4Nothin' on You (feat. Bruno Mars)B.o.Batl hip hop2010.02022‑02‑17104.085.069.0-6.09.074.0268.039.05.079.02010.0Solo
5Magic (feat. Rivers Cuomo)B.o.Batl hip hop2010.02022‑02‑1782.093.055.0-4.035.079.0196.01.034.071.02010.0Solo
6The Time (Dirty Bit)Black Eyed Peasdance pop2010.02022‑02‑17128.081.082.0-8.060.044.0308.07.07.075.02010.0Band/Group
7Imma BeBlack Eyed Peasdance pop2009.02022‑02‑1792.052.060.0-7.031.041.0258.018.037.071.02010.0Band/Group
8Talking to the MoonBruno Marsdance pop2010.02022‑02‑17146.059.050.0-5.011.08.0218.051.03.087.02010.0Solo
9Just the Way You AreBruno Marsdance pop2010.02022‑02‑17109.084.064.0-5.06.042.0221.01.04.086.02010.0Solo

Last rows

titleartisttop genreyear releasedaddedbpmnrgydncedBlivevalduracousspchpoptop yearartist type
993Cruel SummerTaylor Swiftpop2019.02021‑08‑17170.070.055.0-6.011.056.0178.012.016.082.02019.0Solo
994You Need To Calm DownTaylor Swiftpop2019.02020‑06‑2285.067.077.0-6.06.071.0171.01.06.081.02019.0Solo
995SICKO MODETravis Scotthip hop2018.02020‑06‑22155.073.083.0-4.012.045.0313.01.022.086.02019.0Solo
996EARFQUAKETyler, The Creatorhip hop2019.02020‑06‑2280.050.055.0-9.080.041.0190.023.07.085.02019.0Solo
997Boasty (feat. Idris Elba)Wileygrime2019.02020‑06‑22103.077.089.0-5.09.046.0177.01.07.068.02019.0Solo
998Strike a Pose (feat. Aitch)Young T & Bugseyafroswing2019.02020‑08‑20138.058.053.0-6.010.059.0214.01.010.067.02019.0Duo
999The London (feat. J. Cole & Travis Scott)Young Thugatl hip hop2019.02020‑06‑2298.059.080.0-7.013.018.0200.02.015.075.02019.0Solo
1000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1001NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1002NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN